library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)

theme_cowplot2 <- function(...) {
  theme_cowplot(font_size = 12, ...) %+replace%
    theme(strip.background = element_blank(),
          plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution

1 Cluster markers

1.1 Major T.cell markers for cell assign

### load all data ---------------------------------

helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6 <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_major.yaml")

helper_f2 <- function(x) select(unnest(enframe(x, "subtype", "gene"), cols = gene), gene, subtype)
markers_v6_super <- lapply(yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_super.yaml"), helper_f2)

clrs <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_colors.yaml") %>% 
  lapply(function(x) map_depth(x, vec_depth(x)-2, unlist))

names(clrs$patient_id) <- str_remove_all(names(clrs$patient_id), "SPECTRUM-OV-")

meta_tbl <- read_excel("_data/small/MSK SPECTRUM - Single cell RNA-seq_v6.xlsx", sheet = 2) %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>% 
  filter(therapy == "pre-Rx")

signature_tbl <- read_tsv("_data/small/mutational_signatures_summary.tsv") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>% 
  select(patient_id, consensus_signature) %>% 
  bind_rows(tibble(patient_id = unique(sort(meta_tbl$patient_id[!(meta_tbl$patient_id %in% .$patient_id)])), consensus_signature = "NA")) %>% 
  mutate(consensus_signature = ordered(consensus_signature, levels = names(clrs$consensus_signature))) %>%
  arrange(consensus_signature)

seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))

myfeatures <- c("UMAP_1", "UMAP_2", "umapharmony_1", "umapharmony_2", "sample", "RNA_snn_res.0.1", "RNA_snn_res.0.2", "RNA_snn_res.0.3", "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data <- as_tibble(FetchData(seu_obj, myfeatures)) %>% 
  left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy), 
            by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         RNA_snn_res.0.1 = as.character(RNA_snn_res.0.1),
         RNA_snn_res.0.2 = as.character(RNA_snn_res.0.2),
         RNA_snn_res.0.3 = as.character(RNA_snn_res.0.3),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj)) %>% 
  left_join(signature_tbl, by = "patient_id")

patient_id <- sort(unique(plot_data$patient_id))

PTPRC, CD2, CD3D, TRAC, GZMA, NKG7, CD3E, CD3G, CD4, TCF7, CD8A, PRF1, GZMB, CCL5, CCL4, IL32, CD52

1.2 Subtype currated markers

markers_v6_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	CD4.T.dysfunctional	CD4.T.naive	CD4.T.reg	CD8.T	Cycling.T.NK	MT.high.T.NK	NK.CD56	NK.Cytotoxic
1	CD4	CCR7	CD4	CCL4	CDC20	IGKC	CD63	ADGRG1
2	CD40LG	CD4	FOXP3	CD8A	CDK1	MALAT1	CD7	CX3CR1
3	CTLA4	IL7R	IL2RA	CD8B	MKI67	MIAT	FCER1G	FCER1G
4	CXCL13	KLF2	TNFRSF4	CRTAM	PTTG1	MT-ND6	GNLY	FCGR3A
5	FKBP5	TCF7	TRAC	GZMA	TOP2A	MTRNR2L12	KLRC1	FGFBP2
6	IL6ST			GZMB		XIST	KLRD1	GNLY
7	ITM2A			GZMK			KLRF1	GZMH
8	MAF			GZMM			KRT81	IGFBP7
9	NMB			IFNG			KRT86	KLRD1
10	NR3C1			LAG3			NCAM1	KLRF1
11	PDCD1			MT1E			NKG7	NKG7
12	TNFRSF4			MT1X			TYROBP	PLAC8
13	TOX2			MT2A			XCL1	PLEK
14	TSHZ2			PTMS			XCL2	PTGDS
15				TRGC2				SPON2
16								TYROBP

1.3 Subtype cluster markers

marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>% 
  filter(resolution == louvain_resolution)

## Hypergeometric test --------------------------------------

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v6 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v6_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
  
cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_"))

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

formattable::formattable(marker_sheet)

rank	CD4.T.dysfunctional	CD4.T.naive	CD4.T.reg	CD8.T	Cycling.T.NK	doublet.B.cell	doublet.Mast.cell	doublet.Monocyte	doublet.Monocyte_1	doublet.Plasma.cell	Mito.high	Mito.high_1	NK.CD56	NK.Cytotoxic
1	CXCL13	IL7R	TNFRSF4	CD8A	STMN1	IGHM	AREG	CST3	GZMK	SOX4	XIST	WFDC2	GNLY	FGFBP2
2	NMB	CCR7	IL2RA	CD8B	MKI67	MS4A1	LST1	S100A9	STK17A	PTCRA	MTRNR2L12	SLPI	AREG	FCGR3A
3	FKBP5	KLF2	FOXP3	CCL4L2	TUBA1B	MEF2C	IL4I1	S100A8	APOE	MAL	MALAT1	S100A9	TYROBP	SPON2
4	NR3C1	EEF1B2	LTB	GZMK	TOP2A	CD79A	TNFSF13B	SPP1	ATP5F1E	MZB1	IGKC	S100A8	KLRC1	PRF1
5	MAF	TPT1	CTLA4	CCL5	CENPF	BANK1	PCDH9	FTL	SH2D1A	DNTT	MIAT	CD24	FCER1G	KLRF1
6	IL6ST	EEF1A1	RTKN2	GZMH	TUBB	TNFRSF13C	SCN1B	CXCL8	TMA7	STMN1	MT-ND6	KRT18	TRDC	CX3CR1
7	ITM2A	CD40LG	BATF	CCL4	HIST1H4C	RALGPS2	KIT	APOE	NDUFA3	TFDP2	NEAT1	KRT19	KRT81	GNLY
8	CTLA4	TCF7	TNFRSF18	CRTAM	HMGB2	BCL11A	SPINK2	LYZ	C1QB	CD1E	MT-ND3	FTL	KLRD1	NKG7
9	TSHZ2	GPR183	SAT1	TRGC2	ASPM	MARCH1	LTC4S	AIF1	NDUFB1	AC084033.3	MT-ND1	FTH1	XCL1	KLRD1
10	TNFRSF4	LTB	TBC1D4	LAG3	TYMS	VPREB3	CTSH	MARCKS	SPP1	ARPP21	PTPRC	IFITM3	XCL2	PLAC8
11	CD40LG	MAL	GADD45A	IFNG	NUSAP1	TCF4	LINC00299	C1QB	HLA-DRB5	NUCB2	MT-CO3	FGFBP2	IGFBP2	GZMB
12	CORO1B	SELL	TIGIT	PTMS	HMGN2	CD83	ALDOC	FTH1	RNF145	CDK6	NKTR	KRT8	CEBPD	PLEK
13	PDCD1	LDHB	PMAIP1	ITM2C	PCLAF	BASP1	IL1R1	C15orf48	ATP5ME	MAP1A	MT-CO1	SPP1	CLIC3	CLIC3
14	LIMS1	SNHG8	TNFRSF1B	MT1X	H2AFZ	IGHD	TMIGD2	G0S2	CD2	GLUL	MT-ATP6	CLU	TXK	EFHD2
15	CD4	PABPC1	IKZF2	HLA-DPB1	HIST1H1B	LINC02397	TOX2	C1QA	CD3G	MSI2	N4BP2L2	SPINT2	KRT86	GZMH
16	RNF19A	NOSIP	UGP2	GZMA	SMC4	ADAM28	KRT81	FN1	CCL3L1	ADA	MT-ND2	C19orf33	IL2RB	TYROBP
17	RBPJ	NOP53	SOX4	MT2A	TPX2	CD22	GSN	MNDA	EIF3J	AC011893.1	PCSK7	FN1	CTSW	ADGRG1
18	ZBED2	TMEM123	ICOS	HLA-DRB1	PCNA	ARHGAP24	PLAT	BASP1	PET100	MIR181A1HG	MT-CYB	CST3	MATK	CST7
19	DUSP4	LEF1	LINC01943	CST7	UBE2C	SWAP70	CXXC5	APOC1	CD8B	AL357060.1	CD2	CLDN3	KLRB1	PTGDS
20	CPM	EIF3E	TNFRSF9	HLA-DPA1	CLSPN	LINC00926	CCR6	IL1B	BRD9	GRASP	MT-CO2	LINC00861	CCL3	FCER1G
21	AC004585.1	RACK1	IL32	LINC02446	DUT	AFF3	AFF3	CSF3R	CD8A	LDLRAD4	ARID1B	RBP1	CD7	IGFBP7
22	AHI1	AQP3	ARID5B	DTHD1	SMC2	CYBB	IL23R	GRN	IL32	CD1B	AAK1	MT-ND3	CD63	ZEB2
23	ARID5B	UBA52	IFI27	APOBEC3G	ATAD2	HVCN1	DLL1	MS4A6A	GZMA	CLDN5	SPOCK2	GZMH	NKG7	HOPX
24	TOX2	TOMM7	BIRC3	CXCR6	UBE2S	CD24	ARHGAP10	CD14	OTULIN	RCAN1	IKZF1	TIMP1	TMIGD2	CCL3
25	IGFL2	JUNB	LAYN	JAML	CKS1B	FCRL5	SCX	C1QC	CST7	JCHAIN	MT-ND5	MDK	HOPX	S1PR5
26	ICA1	NACA	CD27	MT1E	TMPO	FCRL1	TLE1	GSN	MALAT1	TRDC	SF1	AC004687.1	TNFRSF18	KLF2
27	BATF	SOCS3	CORO1B	CCL3L1	TUBB4B	BLK	C20orf204	PLAUR	LINC01871	ID1	RSRP1	APOE	SRGAP3	AKR1C3
28	CCDC50	FXYD5	TYMP	DUSP2	KNL1	TCL1A	IGFBP4	SPI1	DDX18	AC002454.1	RNF213	ITGB1	LAT2	PRSS23
29	TNFRSF18	SERINC5	CD4	COTL1	CENPE	LY86	SLC4A10	RNASE1	ROMO1	VIPR2	CD44	MT-CYB	GSTP1	MYBL1
30	SRGN	EEF2	PHACTR2	THEMIS	PTTG1	SCIMP	SVIL	CYBB	GPR183	SOCS2	CD3G	NUPR1	KLRC2	C1orf21
31	CD84	TRABD2A	MIR4435-2HG	CD3D	HELLS	PAX5	LIF	SERPINA1	HIST1H4C	CCDC26	DDX17	MT-CO1	LINC00996	CD247
32	RGS1	TNFRSF25	CARD16	GZMM	RRM2	LINC01781	CD300LF	FCGR2A	TPM3	MARCKSL1	MT-ND4L	S100A13	CMC1	ABHD17A
33	CD200	ANK3	ENTPD1	TNFSF9	DEK	SPIB	RORC	CD68	C1QA	NREP	MT-ND4	MT1G	GZMB	TTC38
34	SPOCK2	RIPOR2	SPOCK2	TNIP3	BIRC5	TNFRSF13B	CFH	CSF2RA	GIMAP7	HES4	PPP2R5C	MT-ND2	ITGA1	PTPN12
35	TNFRSF25	ANXA1	SPATS2L	CD3G	HMGB1	PKIG	ENPP1	MS4A7	AAK1	GALNT2	PNISR	MT-ATP6	NCAM1	CTSW
36	SESN3	FLT3LG	LINC02099	CLEC2B	DLGAP5	CD19	TGM2	TNFAIP2	SON	CASC15	B2M	MT-ND4	CXXC5	CEP78
37	CHN1	SARAF	DUSP4	KLRG1	NASP	FAM30A	ZG16B	ALDH2	PDCD4	APBA2	CLEC2D	IFI27	PRF1	ITGB2
38	CH25H	CTSL	STAM	LYST	H2AFX	GNG7	KIAA1324	MAFB	SYNRG	TSHR	STK4	RAB13	IFITM3	ARL4C
39	RILPL2	AP3M2	CTSC	RARRES3	CDK1	WDFY4	CA2	OLR1	COX7C	YBX3	ARGLU1	BEX3	MCTP2	XBP1
40	ZNRF1	ZFAS1	MAGEH1	FABP5	H2AFV	CD40	TNFSF11	CPVL	ITGB2	PFKFB2	FUS	S100A6	KLRF1	FLNA
41	METTL8	TIMP1	S100A4	ZNF683	CKS2	FCRL2	S100A13	FPR1	NDUFA1	TP53INP1	ACAP1	MT-CO3	ZNF683	PTGDR
42	PKM	LINC02273	GLRX	CD27	FABP5	BLNK	PTGER3	SLC11A1	UQCRB	LEF1	CD3D	MT-CO2	SH2D1B	BIN2
43	CTSL	TOB1	PBXIP1	IKZF3	MCM7	COBLL1	DST	TGFBI	APOC1	UHRF1	CXCR4	RNASE1	CCL5	CCL4
44	BHLHE40-AS1	TRADD	AC005224.3	PPP1R14B	CDKN3	TLR10	IRAK3	IGSF6	PSME1	MME	SRSF7	MT-ND1	METRNL	LITAF
45	SLA	EIF4B	BTG3	EOMES	GAPDH	BTK	KIAA1211L	KCTD12	CCL4L2	CHI3L2	RBM39	C1QA	MAFF	GK5
46	BTLA	TSHZ2	F5	APOBEC3C	EZH2	GAPT	APOL4	CXCL16	GZMH	SSBP2	ANKRD12	C1QB	CAPN12	CD300A
47	PHACTR2	GIMAP7	DNPH1	OASL	GTSE1	CPNE5	BCAS1	RAB31	ITM2C	SMIM3	HLA-A	MARCKS	STARD3NL	FGR
48	MIR155HG	DPP4	MAF	PDCD1	HIST1H1D	POU2AF1	COL4A4	IFNGR2	CCL5	AIF1	HLA-E	CTSL	SLC16A3	TXK
49	CYSLTR1	NSA2	HERC5	YBX3	RANBP1	OSBPL10	NEO1	CLEC7A	DDX5	BCL11A	DDX5	APOC1	FOS	GZMM
50	BIRC3	FAU	DUSP16	SH3BGRL3	ZWINT	EBF1	SCRN1	CSF1R	TOMM7	ARRDC2	HLA-C	MALAT1	IFITM2	CHST2

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and mito high clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])

my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]

cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy), 
            by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes),
         ) %>% 
  left_join(signature_tbl, by = "patient_id")
  
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -UMAP_1, -UMAP_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)}

signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")

## compute expression module scores
for (i in 1:length(signature_modules)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
  seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
  print(paste(names(signature_modules)[i], "DONE"))
}

## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"

## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>% 
  as.matrix %>% 
  progeny %>% 
  as.data.frame %>% 
  as.list

names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))

for (i in 1:length(progeny_list)) {
  seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]], 
                             col.name = names(progeny_list)[i])
}

write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy), 
            by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "# cells", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2020-12-05                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib
##  ape            5.3        2019-03-17 [2]
##  assertthat     0.2.1      2019-03-21 [2]
##  backports      1.1.10     2020-09-15 [1]
##  bibtex         0.4.2.2    2020-01-02 [2]
##  Biobase        2.46.0     2019-10-29 [2]
##  BiocGenerics   0.32.0     2019-10-29 [2]
##  bitops         1.0-6      2013-08-17 [2]
##  broom          0.7.2      2020-10-20 [1]
##  callr          3.4.2      2020-02-12 [1]
##  caTools        1.17.1.4   2020-01-13 [2]
##  cellranger     1.1.0      2016-07-27 [2]
##  cli            2.0.2      2020-02-28 [1]
##  cluster        2.1.0      2019-06-19 [3]
##  codetools      0.2-16     2018-12-24 [3]
##  colorblindr  * 0.1.0      2020-01-13 [2]
##  colorspace   * 1.4-2      2019-12-29 [2]
##  cowplot      * 1.0.0      2019-07-11 [2]
##  crayon         1.3.4      2017-09-16 [1]
##  data.table     1.12.8     2019-12-09 [2]
##  DBI            1.1.0      2019-12-15 [2]
##  dbplyr         2.0.0      2020-11-03 [1]
##  desc           1.2.0      2018-05-01 [2]
##  devtools       2.2.1      2019-09-24 [2]
##  digest         0.6.25     2020-02-23 [1]
##  dplyr        * 1.0.2      2020-08-18 [1]
##  ellipsis       0.3.1      2020-05-15 [1]
##  evaluate       0.14       2019-05-28 [2]
##  fansi          0.4.1      2020-01-08 [2]
##  farver         2.0.3      2020-01-16 [1]
##  fitdistrplus   1.0-14     2019-01-23 [2]
##  forcats      * 0.5.0      2020-03-01 [1]
##  formattable    0.2.0.1    2016-08-05 [1]
##  fs             1.5.0      2020-07-31 [1]
##  future         1.15.1     2019-11-25 [2]
##  future.apply   1.4.0      2020-01-07 [2]
##  gbRd           0.4-11     2012-10-01 [2]
##  gdata          2.18.0     2017-06-06 [2]
##  generics       0.0.2      2018-11-29 [2]
##  ggplot2      * 3.3.2      2020-06-19 [1]
##  ggrepel        0.8.1      2019-05-07 [2]
##  ggridges       0.5.2      2020-01-12 [2]
##  globals        0.12.5     2019-12-07 [2]
##  glue           1.3.2      2020-03-12 [1]
##  gplots         3.0.1.2    2020-01-11 [2]
##  gridExtra      2.3        2017-09-09 [2]
##  gtable         0.3.0      2019-03-25 [2]
##  gtools         3.8.1      2018-06-26 [2]
##  haven          2.3.1      2020-06-01 [1]
##  hms            0.5.3      2020-01-08 [1]
##  htmltools      0.4.0      2019-10-04 [2]
##  htmlwidgets    1.5.1      2019-10-08 [2]
##  httr           1.4.2      2020-07-20 [1]
##  ica            1.0-2      2018-05-24 [2]
##  igraph         1.2.5      2020-03-19 [1]
##  irlba          2.3.3      2019-02-05 [2]
##  jsonlite       1.7.1      2020-09-07 [1]
##  KernSmooth     2.23-16    2019-10-15 [3]
##  knitr          1.26       2019-11-12 [2]
##  labeling       0.3        2014-08-23 [2]
##  lattice        0.20-38    2018-11-04 [3]
##  lazyeval       0.2.2      2019-03-15 [2]
##  leiden         0.3.1      2019-07-23 [2]
##  lifecycle      0.2.0      2020-03-06 [1]
##  listenv        0.8.0      2019-12-05 [2]
##  lmtest         0.9-37     2019-04-30 [2]
##  lsei           1.2-0      2017-10-23 [2]
##  lubridate      1.7.9.2    2020-11-13 [1]
##  magrittr     * 2.0.1      2020-11-17 [1]
##  MASS           7.3-51.5   2019-12-20 [3]
##  Matrix         1.2-18     2019-11-27 [3]
##  memoise        1.1.0      2017-04-21 [2]
##  metap          1.2        2019-12-08 [2]
##  mnormt         1.5-5      2016-10-15 [2]
##  modelr         0.1.8      2020-05-19 [1]
##  multcomp       1.4-12     2020-01-10 [2]
##  multtest       2.42.0     2019-10-29 [2]
##  munsell        0.5.0      2018-06-12 [2]
##  mutoss         0.1-12     2017-12-04 [2]
##  mvtnorm        1.0-12     2020-01-09 [2]
##  nlme           3.1-143    2019-12-10 [3]
##  npsurv         0.4-0      2017-10-14 [2]
##  numDeriv       2016.8-1.1 2019-06-06 [2]
##  pbapply        1.4-2      2019-08-31 [2]
##  pillar         1.4.6      2020-07-10 [1]
##  pkgbuild       1.0.6      2019-10-09 [2]
##  pkgconfig      2.0.3      2019-09-22 [1]
##  pkgload        1.0.2      2018-10-29 [2]
##  plotly         4.9.1      2019-11-07 [2]
##  plotrix        3.7-7      2019-12-05 [2]
##  plyr           1.8.5      2019-12-10 [2]
##  png            0.1-7      2013-12-03 [2]
##  prettyunits    1.1.1      2020-01-24 [1]
##  processx       3.4.2      2020-02-09 [1]
##  progeny      * 1.11.3     2020-10-22 [1]
##  ps             1.3.2      2020-02-13 [1]
##  purrr        * 0.3.4      2020-04-17 [1]
##  R.methodsS3    1.7.1      2016-02-16 [2]
##  R.oo           1.23.0     2019-11-03 [2]
##  R.utils        2.9.2      2019-12-08 [2]
##  R6             2.4.1      2019-11-12 [1]
##  RANN           2.6.1      2019-01-08 [2]
##  rappdirs       0.3.1      2016-03-28 [2]
##  RColorBrewer   1.1-2      2014-12-07 [2]
##  Rcpp           1.0.4      2020-03-17 [1]
##  RcppAnnoy      0.0.16     2020-03-08 [1]
##  RcppParallel   4.4.4      2019-09-27 [2]
##  Rdpack         0.11-1     2019-12-14 [2]
##  readr        * 1.4.0      2020-10-05 [1]
##  readxl       * 1.3.1      2019-03-13 [2]
##  rematch        1.0.1      2016-04-21 [2]
##  remotes        2.1.0      2019-06-24 [2]
##  reprex         0.3.0      2019-05-16 [2]
##  reshape2       1.4.3      2017-12-11 [2]
##  reticulate     1.14       2019-12-17 [2]
##  rlang          0.4.8      2020-10-08 [1]
##  rmarkdown      2.0        2019-12-12 [2]
##  ROCR           1.0-7      2015-03-26 [2]
##  rprojroot      1.3-2      2018-01-03 [2]
##  rstudioapi     0.11       2020-02-07 [1]
##  rsvd           1.0.3      2020-02-17 [1]
##  Rtsne          0.15       2018-11-10 [2]
##  rvest          0.3.6      2020-07-25 [1]
##  sandwich       2.5-1      2019-04-06 [2]
##  scales         1.1.0      2019-11-18 [2]
##  sctransform    0.2.1      2019-12-17 [2]
##  SDMTools       1.1-221.2  2019-11-30 [2]
##  sessioninfo    1.1.1      2018-11-05 [2]
##  Seurat       * 3.1.2      2019-12-12 [2]
##  sn             1.5-4      2019-05-14 [2]
##  stringi        1.5.3      2020-09-09 [1]
##  stringr      * 1.4.0      2019-02-10 [1]
##  survival       3.1-8      2019-12-03 [3]
##  testthat       2.3.2      2020-03-02 [1]
##  TFisher        0.2.0      2018-03-21 [2]
##  TH.data        1.0-10     2019-01-21 [2]
##  tibble       * 3.0.4      2020-10-12 [1]
##  tidyr        * 1.1.2      2020-08-27 [1]
##  tidyselect     1.1.0      2020-05-11 [1]
##  tidyverse    * 1.3.0      2019-11-21 [2]
##  tsne           0.1-3      2016-07-15 [2]
##  usethis        1.5.1      2019-07-04 [2]
##  uwot           0.1.5      2019-12-04 [2]
##  vctrs          0.3.5      2020-11-17 [1]
##  viridis      * 0.5.1      2018-03-29 [2]
##  viridisLite  * 0.3.0      2018-02-01 [2]
##  withr          2.3.0      2020-09-22 [1]
##  xfun           0.12       2020-01-13 [2]
##  xml2           1.3.2      2020-04-23 [1]
##  yaml           2.2.1      2020-02-01 [1]
##  zoo            1.8-7      2020-01-10 [2]
##  source                                 
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.3)                         
##  CRAN (R 3.6.2)                         
##  Bioconductor                           
##  Bioconductor                           
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
##  CRAN (R 3.6.2)                         
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## 
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2020-12-05

1 Cluster markers

1.1 Major T.cell markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet and mito high clusters

3.1 UMAP

3.2 add module scores and pathway scores

3.3 marker heatmap

3.4 composition

3.4.1 per site

3.4.2 per sample

3.4.3 site specific cluster enrichment

4 session info